Only a handful set of signaling pathways (FGF, BMP, Wnt, Hh, Notch, etc) are repeatedly utilized to control almost all aspects of cell-cell communication from early embryonic development to adult tissue homeostasis. How this small set of pathways controls such a large number of phenomena is poorly understood. We and others recently showed that signal response is not binary, and that gene expression depends on many parameters of a cell?s signaling history, including duration, timing, and rate of signal change. Therefore, different responses to the same signaling molecules may be in part attributed to different time courses of exposure. The primary goal of the proposed research is to develop a predictive understanding of how the signaling history of a cell controls its fate, focusing on early cell fate decisions in human pluripotent stem cells. To decipher how information is encoded in dynamic signals we will take a highly interdisciplinary approach that combines gene editing, quantitative fluorescence microscopy, engineering of the stem cell environment, computational analysis, and mathematical modeling. The proposed interrelated goals build on previously published work combining these approaches by the PI and recent preliminary data from the laboratory. First, we will determine population level signaling dynamics in response to FGF. The quantitative characteristics of FGF signaling are not well understood despite playing a crucial role in pluripotency maintenance and mesendoderm differentiation, and this information is important in laying the foundation for the second project. Second, we will go beyond population level dynamics of a single pathway, and measure signaling through multiple pathways simultaneously in individual cells to identify precise features of combinatorial signaling that are predictive of fate. Specifically, we will create a single cell line expressing four of our published constructs to visualize each of the paracrine pathways involved in early cell fate (Wnt, BMP, Activin/Nodal, and FGF), and utilize our custom image analysis software for tracking cells through many days of differentiation. This will generate unique high-dimensional data in the form single-cell multi- pathway signaling histories linked to cell fate. We will then use data science methods to determine signaling features that predict cell fate. Third, we will investigate the interplay between tissue mechanics and cell signaling. Mesoderm differentiation is closely linked to an epithelial-mesenchymal transition and dramatic changes in intercellular forces. By combining our signaling assays with force manipulation and force measurement, we will gain biophysical insight into how FGF regulates intercellular tension and adhesion, and how tension and adhesion modulate the Wnt response. The ultimate goal is to obtain a quantitative understanding of the complex interplay between signaling dynamics, cell mechanics, and cell fate, and exploit this knowledge for wide ranging therapeutic applications including optimized protocols for directed stem cell differentiation and more effective use of drugs that target signaling pathways.